Modern computerized search systems can contain vast amounts of data. In order to be useful, they must return search results to a user in a reasonable amount of time. One application for computerized search systems is that of finding geographic entities such as an address, street, area, point, businesses, or list of cities that are located near a given starting point. For example, with a system containing a database of homes and businesses with corresponding addresses or geographical locations within a city, a user may want to locate banks, automated teller machines (ATMs), or restaurants which are close to a specific address or business within the city. Because the user may be waiting for the results of the search in real-time, the search results must be returned very quickly. In addition, the results should preferably be presented in an ordered format according to the usefulness to the user. For example, some businesses might have a sale or special offering which should be presented to the user before other businesses which do not have a sale or special.
Known search systems typically pre-define the search radius to be used when searching. If no match is found, the user must manually increment the search radius by a pre-defined amount and repeat the search. These types of systems are generally slow because they require multiple unnecessary searches and user interaction going from a smaller to a larger search radius. In addition, by pre-defining the search radius, traditional systems may return a larger number of records than necessary for densely populated areas such as cities. Sorting a large number of records is computationally intensive and will typically delay getting search results to the user.
Therefore, there is a need for an efficient way of searching a large amount of data and returning the search results to a user in an ordered format.